一种基于单阶段深度学习的液滴数字 PCR 图像自动分析方法,Analyst

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一种基于单阶段深度学习的液滴数字 PCR 图像自动分析方法
Analyst ( IF 3.6 ) Pub Date : 2023-06-07 , DOI: 10.1039/d3an00615h
Yuanyang Yao ₁ , Shuhao Zhao ₁ , Yan Liang ₁ , Fei Hu ₁ , Niancai Peng ₁

Affiliation

与基于芯片的 dPCR 相比，基于液滴的 dPCR 具有许多优势，例如更低的处理成本、更高的液滴密度、更高的通量，同时需要更少的样品。然而，液滴位置的随机性、不均匀的照明和不清晰的液滴边界使得自动图像分析具有挑战性。目前用于计数大量微滴的大多数方法都依赖于流量检测。传统的机器视觉算法无法从复杂的背景中提取目标的全部信息。一些两阶段方法首先对液滴进行定位，然后根据其灰度值进行分类，需要高质量的成像。在本研究中，我们通过改进一种名为YOLOv5的单阶段深度学习算法并将其应用于检测任务来解决这些局限性，以实现单阶段检测。我们引入了注意力机制模块来提高小目标的检测率，并使用新的损失函数来加快训练过程。此外，我们采用了网络修剪方法来促进模型在移动设备上的部署，同时保持其性能。我们用捕获的基于液滴的 dPCR 图像验证了模型，发现改进的模型可以准确识别复杂背景中的阴性和阳性液滴，错误率为 0.65%。该方法具有检测速度快、准确率高、能够在移动设备或云平台上使用的特点。总体而言，该研究提出了一种在大规模微滴图像中检测液滴的新方法，并为基于液滴的 dPCR 中准确、高效的液滴计数提供了一种有前景的解决方案。

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A one-stage deep learning based method for automatic analysis of droplet-based digital PCR images

Droplet-based dPCR offers many advantages over chip-based dPCR, such as lower processing cost, higher droplet density, higher throughput, while requiring less sample. However, the stochastic nature of droplet locations, uneven illuminations, and unclear droplet boundaries make automatic image analysis challenging. Most methods currently used to count a large amount of microdroplets rely on flow detection. Conventional machine vision algorithms cannot extract all information of the targets from complex backgrounds. Some two-stage methods, which first locate and then classify droplets according to their grayscale values, require high-quality imaging. In this study, we addressed these limitations by improving a one-stage deep learning algorithm named YOLOv5 and applying it to the detection task to realize one-stage detection. We introduced an attention mechanism module to increase the detection rate of small targets and used a new loss function to speed up the training process. Furthermore, we employed a network pruning method to facilitate the deployment of the model on mobile devices while preserving its performance. We validated the model with captured droplet-based dPCR images and found that the improved model accurately identified negative and positive droplets in complex backgrounds with an error rate of 0.65%. This method is characterized by its fast detection speed, high accuracy, and ability to be used on mobile devices or cloud platforms. Overall, the study presents a novel approach for detecting droplets in large-scale microdroplet images and provides a promising solution for accurate and efficient droplet counting in droplet-based dPCR.

更新日期：2023-06-12

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